fixed a lot of things and actually got it to work

README-GLaDOS-TYS-Wyoming-and-ROCM.md

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+# GLaDOS TTS - ROCm Deployment (AMD GPU)
+
+This guide covers running the Wyoming TTS server with AMD GPU acceleration via ROCm.
+
+## Prerequisites
+
+- Linux host with an AMD GPU supported by ROCm
+- ROCm kernel drivers installed (`amdgpu` module loaded)
+- Docker with `amdgpu` device support
+
+Verify ROCm is available on the host:
+
+```bash
+ls /dev/kfd /dev/dri
+rocm-smi  # if ROCm tools are installed
+```
+
+## Running with docker-compose
+
+```bash
+docker compose up glados-tts-rocm
+```
+
+This uses the `Dockerfile.rocm` image with:
+- PyTorch ROCm backend (device mapped to `cuda` internally)
+- Half-precision (`--half`) enabled for ~2x speedup
+- Model pre-loaded at startup (`--preload`)
+- MIOpen convolution solver caching for faster repeated inference
+
+## Environment Variables
+
+| Variable | Default | Description |
+|----------|---------|-------------|
+| `DEVICE` | `rocm` | Set to `rocm` for AMD GPU |
+| `HALF` | `true` | Use float16 inference |
+| `PRELOAD` | `true` | Load model at startup |
+| `MODEL_DIR` | `/data` | Model storage directory |
+| `URI` | `tcp://0.0.0.0:10200` | Wyoming server URI |
+
+ROCm-specific optimizations are applied automatically:
+- `MIOPEN_FIND_MODE=1` - cache convolution solver solutions
+- `MIOPEN_DEBUG_CONV_GEMM=0` - default GEMM mode
+
+## Device Mapping
+
+The server maps `rocm` to `cuda` internally since PyTorch ROCm uses the CUDA API compatibility layer. No code changes are needed.
+
+## Troubleshooting
+
+**No GPU detected in container logs**:
+```bash
+# Verify host has ROCm devices
+ls -la /dev/kfd /dev/dri
+
+# Check if amdgpu module is loaded
+lsmod | grep amdgpu
+```
+
+**Out of memory**: Reduce `HALF=false` or ensure sufficient GPU VRAM (model requires ~4GB).